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Knee Osteoarthritis (KOA) is the third most prevalent MusculoskeletalDisorder (MSD) after neck and back pain. To monitor such a severe MSD, asegmentation map of the femur, tibia and tibiofemoral cartilage is usuallyaccessed using the automated segmentation algorithm from the Magnetic ResonanceImaging (MRI) of the knee. But, in recent works, such segmentation isconceivable only from the multistage framework thus creating data handlingissues and needing continuous manual inference rendering it unable to make aquick and precise clinical diagnosis. In order to solve these issues, in thispaper the Multi-Resolution Attentive-Unet (MtRA-Unet) is proposed to segmentthe femur, tibia and tibiofemoral cartilage automatically. The proposed workhas included a novel Multi-Resolution Feature Fusion (MRFF) and ShapeReconstruction (SR) loss that focuses on multi-contextual information andstructural anatomical details of the femur, tibia and tibiofemoral cartilage.Unlike previous approaches, the proposed work is a single-stage and end-to-endframework producing a Dice Similarity Coefficient (DSC) of 98.5% for the femur,98.4% for the tibia, 89.1% for Femoral Cartilage (FC) and 86.1% for TibialCartilage (TC) for critical MRI slices that can be helpful to clinicians forKOA grading. The time to segment MRI volume (160 slices) per subject is 22 sec.which is one of the fastest among state-of-the-art. Moreover, comprehensiveexperimentation on the segmentation of FC and TC which is of utmost importancefor morphology-based studies to check KOA progression reveals that the proposedmethod has produced an excellent result with binary segmentation

Segmentation of tibiofemoral joint tissues from knee MRI using MtRA-Unet and incorporating shape information: Data from the Osteoarthritis Initiative